University of Portsmouth

About: University of Portsmouth is a education organization based out in Portsmouth, Portsmouth, United Kingdom. It is known for research contribution in the topics: Population & Galaxy. The organization has 5452 authors who have published 14256 publications receiving 424346 citations. The organization is also known as: Portsmouth and Gosport School of Science and Art & Portsmouth and Gosport School of Science and the Arts.

You cannot hide your telephone lies: Providing a model statement as an aid to detect deception in insurance telephone calls

Abstract: Deception research regarding insurance claims is rare but relevant given the financial loss in terms of fraud. In Study 1, a field study in a large multinational insurance fraud detection company, truth telling mock claimants (N = 19) and lying mock claimants (N = 21) were interviewed by insurance company telephone operators. These operators classified correctly only 50% of these truthful and lying claimants, but their task was particularly challenging: Claimants said little, and truthful and deceptive statements did not differ in quality (measured with Criteria-Based Content Analysis [CBCA]) or plausibility. In Study 2, a laboratory experiment, participants in the experimental condition (N = 43) were exposed to an audiotaped truthful and detailed account of an event that was unrelated to insurance claims (a day at the motor races). The number of words, quality of the statement (measured with CBCA), and plausibility of the participants' accounts were compared with participants who were not given a model statement (N = 40). The participants who had listened to the model statement provided longer statements than control participants, truth tellers obtained higher CBCA scores than liars, and only in the model statement condition did truth tellers sound more plausible than liars. Providing participants with a model statement is thus an innovative and successful tool to elicit cues to deception. Providing such a model has the potential to enhance performance in insurance call interviews, and, as we argue, in many other interview settings.

Estimating underlying energy demand trends using UK annual data

Abstract: Employing the Structural Time Series Model (STSM) approach suggested by Harvey (1997), and based on annual data for the UK from 1967–2002, this paper reiterates the importance of using a stochastic rather than a linear deterministic trend formulation when estimating energy demand models, a practice originally established by Hunt et al. (2003a, 2003b) using quarterly UK data. The findings confirm that important non-linear and stochastic trends are present as a result of technical change and other exogenous factors driving demand, and that a failure to account for these trends will lead to biased estimates of the long-run price and income elasticities. The study also establishes that, provided these effects are allowed for, the estimated long-run elasticities are robust to the different data frequencies used in the modelling.

New specimens of Pterosauria (Reptilia) with soft parts with implications for pterosaurian anatomy and locomotion

Abstract: Abstract New specimens of pterosaurs with soft-part preservation from the Solnhofen Lithographic Limestone (S Germany) and the Crato Formation (northeastern Brazil) yield hitherto unknown and unexpected details of pterosaur anatomy: the presence and internal anatomy of softtissue crests, the internal anatomy of the brachiopatagium, including a blood vessel system and structural details of foot and hand. Some consequences for pterosaurian flight, thermoregulation and aspects of evolution are discussed.

Human Hand Motion Analysis With Multisensory Information

Abstract: In order to study and analyze human hand motions that contain multimodal information, a generalized framework integrating multiple sensors is proposed and consists of modules of sensor integration, signal preprocessing, correlation study of sensory information, and motion identification. Three types of sensors are integrated to simultaneously capture the finger angle trajectories, the hand contact forces, and the forearm electromyography (EMG) signals. To facilitate the rapid acquisition of human hand tasks, methods to automatically synchronize and segment manipulation primitives are developed in the signal preprocessing module. Correlations of the sensory information are studied by using Empirical Copula and demonstrate that there exist significant relationships between muscle signals and finger trajectories and between muscle signals and contact forces. In addition, recognizing different hand grasps and manipulations based on the EMG signals is investigated by using Fuzzy Gaussian Mixture Models (FGMMs) and results of comparative experiments show FGMMs outperform Gaussian Mixture Models and support vector machine with a higher recognition rate. The proposed framework integrating the state-of-the-art sensor technology with the developed algorithms provides researchers a versatile and adaptable platform for human hand motion analysis and has potential applications especially in robotic hand or prosthetic hand control and human-computer interaction.